scholarly journals Automatic Classification Method of Quaternary Lithology in Vegetation Cover Area Combining Spectral, Textural, Topographic, Geothermal, and Vegetation Features

2021 ◽  
Author(s):  
Sipeng Han ◽  
Shuang Shuai ◽  
Wangchuan Guo ◽  
Peng Yang
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Transport Model ◽  
Radiation Transport ◽  
Principal Component ◽  
Automatic Classification ◽  
Classification Method ◽  
Occurrence Matrix

To explore the automatic classification method of Quaternary lithology in vegetation covered areas is significantly helpful to improve the efficiency of Quaternary lithology mapping. Due to the vegetation cover and human modification effects, the traditional lithology identification methods based on image spectra and textures are often challenging to be effective. This paper uses multi-source remote sensing data such as OLI, TIRS, and ASTER GDEM to extract multiple types of spectral (SPEC), textural (TEX), topographic (TOPO), geothermal (TEM), and vegetation (VEG) based on principal component transform, gray co-occurrence matrix, topographic factor calculation, thermal radiation transport model and vegetation index in the Quaternary distribution area of Viet Chi, Vietnam. Remote sensing features were selected and combined to form 16 kinds of classification datasets. The lithological units was automatically classified using the random forest method, The method’s accuracy was evaluated to study the effectiveness of multi-type remote sensing features on the automatic classification of Quaternary lithology in vegetation cover area. The results show that the geothermal, textural, and topographic features can effectively improve the lithological classification accuracy, and the overall classification accuracy is improved by 0.32%, 0.87%, and 2.25%, respectively, compared with the use of spectral data alone. Among the 16 classification datasets constructed, the dataset combining spectral, textural, topographic, and geothermal features (SPEC+ TEX+ TOPO+ TEM) obtained the highest automatic lithology classification accuracy of 80.99%. This study can provide a technical idea for rapid differentiation of regional Quaternary surface sediment lithologies.

scholarly journals Spatial and temporal variation of vegetation cover in Al- Soudah area and its relationship to precipitation during the period (2014- 2018 AD) using remote sensing and geographic information systems: التباين المكاني والزمني للغطاء النباتي بمنطقة السودة وعلاقته بالتساقط خلال الفترة (2014- 2018م) باستخدام الاستشعار عن بعد ونظم المعلومات الجغرافية

2021 ◽  
Vol 5 (3) ◽  
pp. 107-87
Author(s):  
Fadi Abdullah alanazi, Yaser Rashed Alzannan, Faten Hamed Na Fadi Abdullah alanazi, Yaser Rashed Alzannan, Faten Hamed Na
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Geographic Information ◽  
Temporal Changes ◽  
Spatial And Temporal Variation ◽  
Spatial And Temporal Changes ◽  
Modified Soil

Souda is one of the important regions in Saudi Arabia in terms of spatial and temporal changes in vegetation cover; It includes the National Park, which is a leading tourist destination and one of the most beautiful parks in it. by tracking the spatial and temporal changes of vegetation cover by integrating remote sensing and geographic information systems, through the application of the modified soil vegetation index MSAVI during the period (2014- 2018), it became clear the decrease in the quantity and density of vegetation cover in the area. Thus, the study concluded that this indicator is one of the best indicators that can be used to extract vegetation cover from satellite images.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

2021 ◽  
Vol 25 (9) ◽  
pp. 30-37
Author(s):  
N.N. Sliusar ◽  
A.P. Belousova ◽  
G.M. Batrakova ◽  
R.D. Garifzyanov ◽  
M. Huber-Humer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Google Earth ◽  
The State ◽  
Landsat 8 ◽  
The Earth

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

scholarly journals A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images

2020 ◽  
Vol 12 (22) ◽  
pp. 3845
Author(s):  
Zhiyu Xu ◽  
Yi Zhou ◽  
Shixin Wang ◽  
Litao Wang ◽  
Feng Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Classification Accuracy ◽  
Green Space ◽  
Urban Green Space ◽  
Classification Method ◽  
Deciduous Trees ◽  
Remote Sensing Images ◽  
Urban Green

The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification.

Remote Sensing of Arctic Vegetation: Relations between the NDVI, Spatial Resolution and Vegetation Cover on Boothia Peninsula, Nunavut

ARCTIC ◽  
2009 ◽  
Vol 61 (1) ◽  
pp. 1 ◽  
Author(s):  
Gita J. Laidler ◽  
Paul M. Treitz ◽  
David M. Atkinson
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Moisture Regime ◽  
Percent Cover ◽  
Multispectral Data ◽  
Arctic Vegetation

Arctic tundra environments are thought to be particularly sensitive to changes in climate, whereby alterations in ecosystem functioning are likely to be expressed through shifts in vegetation phenology, species composition, and net ecosystem productivity (NEP). Remote sensing has shown potential as a tool to quantify and monitor biophysical variables over space and through time. This study explores the relationship between the normalized difference vegetation index (NDVI) and percent-vegetation cover in a tundra environment, where variations in soil moisture, exposed soil, and gravel till have significant influence on spectral response, and hence, on the characterization of vegetation communities. IKONOS multispectral data (4 m spatial resolution) and Landsat 7 ETM+ data (30 m spatial resolution) were collected for a study area in the Lord Lindsay River watershed on Boothia Peninsula, Nunavut. In conjunction with image acquisition, percent cover data were collected for twelve 100 m × 100 m study plots to determine vegetation community composition. Strong correlations were found for NDVI values calculated with surface and satellite sensors, across the sample plots. In addition, results suggest that percent cover is highly correlated with the NDVI, thereby indicating strong potential for modeling percent cover variations over the region. These percent cover variations are closely related to moisture regime, particularly in areas of high moisture (e.g., water-tracks). These results are important given that improved mapping of Arctic vegetation and associated biophysical variables is needed to monitor environmental change.

scholarly journals Mapeamento e Quantificação da Cobertura Vegetal no Agreste Central de Pernambuco Utilizando Técnicas de Empilhamento e o NDVI (Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique)

2011 ◽  
Vol 3 (3) ◽  
pp. 157
Author(s):  
Daniel Rodrigues Lira ◽  
Maria do Socorro Bezerra de Araújo ◽  
Everardo Valadares De Sá Barretto Sampaio ◽  
Hewerton Alves da Silva
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Digital Processing ◽  
Sparse Vegetation ◽  
New Sensors

O mapeamento e monitoramento da cobertura vegetal receberam consideráveis impulsos nas últimas décadas, com o advento do sensoriamento remoto, processamento digital de imagens e políticas de combate ao desmatamento, além dos avanços nas pesquisas e gerações de novos sensores orbitais e sua distribuição de forma mais acessível aos usuários, tornam as imagens de satélite um dos produtos do sensoriamento remoto mais utilizado para análises da cobertura vegetal das terras. Os índices de cobertura vegetal deste trabalho foram obtidos usando o NDVI - Normalized Difference Vegetation Index para o Agreste central de Pernambuco indicou 39,7% de vegetação densa, 13,6% de vegetação esparsa, 14,3% de vegetação rala e 10,5% de solo exposto. O NDVI apresentou uma caracterização satisfatória para a classificação do estado da vegetação do ano de 2007 para o Agreste Central pernambucano, porém ocorreu uma confusão com os índices de nuvens, sombras e solos exposto, necessitando de uma adaptação na técnica para um melhor aprimoramento da diferenciação desses elementos, constituindo numa recombinação de bandas após a elaboração e calculo do NDVI.Palavras-chave: Geoprocessamento; sensoriamento remoto; índice de vegetação. Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique ABSTRACTIn recent decades, advanced techniques for mapping and monitoring vegetation cover have been developed with the advent of remote sensing. New tools for digital processing, the generation of new sensors and their orbital distribution more accessible have facilitated the acquisition and use of satellite images, making them one of the products of remote sensing more used for analysis of the vegetation cover. The aim of this study was to assess the vegetation cover from Central Agreste region of Pernambuco State, using satellite images TM / LANDSAT-5. The images were processed using the NDVI (Normalized Difference Vegetation Index) technique, generating indexes used for classification of vegetation in dense, sparse and scattered. There was a proportion of 39.7% of dense vegetation, 13.6% of sparse vegetation, 14.3% of scattered vegetation and 10.5% of exposed soil. NDVI technique has been used as a useful tool in the classification of vegetation on a regional scale, however, needs improvement to a more precise differentiation among levels of clouds, shadow, exposed soils and vegetation. Keywords: Geoprocessing, remote sensing, vegetation index

scholarly journals SATELLITE-DRIVEN ASSESSMENT OF SURFACE URBAN HEAT ISLANDS IN THE CITY OF ZAGREB, CROATIA

2020 ◽  
Vol V-3-2020 ◽  
pp. 757-764
Author(s):  
A. Krtalić ◽  
A. Kuveždić Divjak ◽  
K. Čmrlec
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Principal Component ◽  
Urban Heat Islands ◽  
City Centre ◽  
Landsat 8 ◽  
Heat Islands ◽  
Critical Areas ◽  
Urban Heat ◽  
The City

Abstract. This study aims to assess surface urban heat islands (SUHIs) pattern over the city of Zagreb, Croatia, based on satellite (optical and thermal) remote sensing data. The spatio-temporal identification of SUHIs is analysed using the 12 sets of Landsat 8 imagery acquired during 2017 (in each month of the year). Vegetation cover within the city boundaries is extracted by using Principal Component Analysis (PCA) data fusion method on calculated three vegetation indices (VI): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Ratio Vegetation Index (RVI) for each set of bands. The first principal component was used to compute the land surface temperature (LST) and deductive Environmental Criticality Index (ECI). As expected, the relationship between LST and all VI scores shows a negative correlation and is most negative with RVI. The environmentally critical areas and the patterns of seasonal variations of the SUHIs in the city of Zagreb were identified based on the LST, ECI and vegetation cover. The city centre, an industrial area in the eastern part and an area with shopping centers and commercial buildings in the western part of the city were identified as the most critical areas.

scholarly journals MONITORING DESERTIFICATION IN SOME REGIONS OF IRAQ USING GIS TECHNIQUES

2021 ◽  
Vol 52 (3) ◽  
pp. 620-625
Author(s):  
Y. K. Al-Timimi
Keyword(s):  
Remote Sensing ◽  
Environmental Economic ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Marked Decrease ◽  
Social Systems ◽  
Sand Dunes ◽  
Rate Of Increase ◽  
Remote Sensing Techniques

Desertification is one of the phenomena that threatening the environmental, economic, and social systems. This study aims to evaluate and monitor desertification in the central parts of Iraq between the Tigris and Euphrates rivers through the use of remote sensing techniques and geographic information systems. The Normalized difference vegetation index NDVI and the crust index CI were used, which were applied to two of the Landsat ETM + and OLI satellite imagery during the years 1990 and 2019. The research results showed that the total area of ​​the vegetation cover was 2620 km2 in 1990, while there was a marked decrease in the area Vegetation cover 764 km2 in 2019, accounting for 34.8% (medium desertification) and 10.2% (high desertification), respectively. Also, the results showed that sand dunes occupied an area of ​​767 km2 in 1990, while the area of ​​sand dunes increased to 1723 km2 in 2019, with a rate of 10.2%) medium desertification (and 22.9% (severe desertification), respectively. It was noted that the overall rate of decrease in vegetation cover was 21.33 km2year-1 while the overall rate of increase in ground erosion in the area is 10.99 km2year-1.

scholarly journals Fractional Vegetation Cover Change Detection In Megacities Using Landsat Time-Series Images: A Case Study Of Hanoi City (Vietnam) During 1986-2019

2019 ◽  
Vol 12 (4) ◽  
pp. 175-187
Author(s):  
Thanh Tien Nguyen
Keyword(s):  
Rural Areas ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Classification Method ◽  
Landsat 8 ◽  
Landsat Images ◽  
Fractional Vegetation Cover ◽  
Multi Temporal

The objective of the study is to assess changes of fractional vegetation cover (FVC) in Hanoi megacity in period of 33 years from 1986 to 2016 based on a two endmember spectral mixture analysis (SMA) model using multi-spectral and multi-temporal Landsat-5 TM and -8 OLI images. Landsat TM/OLI images were first radiometrically corrected. FVC was then estimated by means of a combination of Normalized Difference Vegetation Index (NDVI) and classification method. The estimated FVC results were validated using the field survey data. The assessment of FVC changes was finally carried out using spatial analysis in GIS. A case study from Hanoi city shows that: (i) the proposed approach performed well in estimating the FVC retrieved from the Landsat-8 OLI data and had good consistency with in situ measurements with the statistically achieved root mean square error (RMSE) of 0.02 (R 2 =0.935); (ii) total FVC area of 321.6 km 2 (accounting for 9.61% of the total area) was slightly reduced in the center of the city, whereas, FVC increased markedly with an area of 1163.6 km 2 (accounting for 34.78% of the total area) in suburban and rural areas. The results from this study demonstrate the combination of NDVI and classification method using Landsat images are promising for assessing FVC change in megacities.

scholarly journals RELATIONSHIP OF SPECTRAL REFLECTANCE AND NDVI TO SOME SOIL PROPERTIES OF BRICKS FACTORIES SOILS IN NAHRAWAN AREA,BAGHDAD IRAQ

2019 ◽  
Vol 50 (3) ◽  
Author(s):  
R. K. Abdullatiff
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Spectral Reflectivity ◽  
Tigris River ◽  
The Difference ◽  
Remote Sensing Techniques ◽  
The One ◽  
Relationship Of

A study was conducted to investigate the effect of the brick industry on the environmental system of these project soils of the brick factories in Alnahrawan district. Remote sensing techniques was used to study the relationship between the spectral reflectivity and the vegetative index on the one hand and some surface soil characters of the project and to determine the variation in vegetation cover for the same area and for two different periods.Ten sites were selected to study spectral reflectivity under similar geomorphological conditions near the brickworks project in the Anahrawan district with an area of 10,000 hectares. Soil samples were taken from the surface and at a depth of 0-30 cm. Some chemical and physical characters of research soil were analyzed in the soil department laboratories, college of Agriculture, Baghdad University.Several satellite images taken from the satellite Land sat (ETM) 2013 and another from same satellite in 1990 T.M to determining the change between the two periods. After obtaining remote sensing data (reflectivity and vegetation index).the correlation analysis was carried out between these data. It was observed that the soil salinity values were decreased due to the drainage that the area was confined between the Tigris River and the Diyala tributary which leads to good natural drainage.The attached tables indicate that thedigital numbers of the soil sampling sites in 2013 are highly significant correlated, While some of the characters did not show the use of this region industrially. After calculating the difference between the two images to determine the change. A 100% change was observed and the vegetation cover was sharply reduced between the two images. as well as the extension of the land of empty land, although these lands are still suitable for agriculture.

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